RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation

• URL: http://arxiv.org/abs/2306.11368v4
• Date: Fri, 21 Jun 2024 07:53:40 GMT
• Title: RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation
• Authors: Ruohong Mei, Wei Sui, Jiaxin Zhang, Xue Qin, Gang Wang, Tao Peng, Cong Yang,
• Abstract summary: RoMe is a novel framework designed for the robust reconstruction of large-scale road surfaces. Our evaluations underscore RoMe's superiority in terms of speed, accuracy, and robustness. RoMe's capability extends beyond mere reconstruction, offering significant value for autolabeling tasks in autonomous driving applications.
• Score: 9.622389166012741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In autonomous driving applications, accurate and efficient road surface reconstruction is paramount. This paper introduces RoMe, a novel framework designed for the robust reconstruction of large-scale road surfaces. Leveraging a unique mesh representation, RoMe ensures that the reconstructed road surfaces are accurate and seamlessly aligned with semantics. To address challenges in computational efficiency, we propose a waypoint sampling strategy, enabling RoMe to reconstruct vast environments by focusing on sub-areas and subsequently merging them. Furthermore, we incorporate an extrinsic optimization module to enhance the robustness against inaccuracies in extrinsic calibration. Our extensive evaluations of both public datasets and wild data underscore RoMe's superiority in terms of speed, accuracy, and robustness. For instance, it costs only 2 GPU hours to recover a road surface of 600*600 square meters from thousands of images. Notably, RoMe's capability extends beyond mere reconstruction, offering significant value for autolabeling tasks in autonomous driving applications. All related data and code are available at https://github.com/DRosemei/RoMe.

Related papers

• ROAD-Waymo: Action Awareness at Scale for Autonomous Driving [17.531603453254434]
  ROAD-Waymo is an extensive dataset for the development and benchmarking of techniques for agent, action, location and event detection in road scenes. Considerably larger and more challenging than any existing dataset (and encompassing multiple cities), it comes with 198k annotated video frames, 54k agent tubes, 3.9M bounding boxes and a total of 12.4M labels.
  arXiv  Detail & Related papers  (2024-11-03T20:46:50Z)
• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting [85.67616000086232]
  StreetSurfGS is first method to employ Gaussian Splatting specifically tailored for scalable urban street scene surface reconstruction. StreetSurfGS utilizes a planar-based octree representation and segmented training to reduce memory costs, accommodate unique camera characteristics, and ensure scalability. To address sparse views and multi-scale challenges, we use a dual-step matching strategy that leverages adjacent and long-term information.
  arXiv  Detail & Related papers  (2024-10-06T04:21:59Z)
• RoadRunner M&M -- Learning Multi-range Multi-resolution Traversability Maps for Autonomous Off-road Navigation [12.835198004089385]
  RoadRunner (M&M) is an end-to-end learning-based framework that directly predicts the traversability and elevation maps at multiple ranges. RoadRunner M&M achieves a significant improvement of up to 50% for elevation mapping and 30% for traversability estimation over RoadRunner.
  arXiv  Detail & Related papers  (2024-09-17T07:21:03Z)
• RoBus: A Multimodal Dataset for Controllable Road Networks and Building Layouts Generation [4.322143509436427]
  We introduce a multimodal dataset with evaluation metrics for controllable generation of Road networks and Building layouts (RoBus) RoBus is the first and largest open-source dataset in city generation so far. We analyze the RoBus dataset statistically and validate the effectiveness against existing road networks and building layouts generation methods. We design new baselines that incorporate urban characteristics, such as road orientation and building density, in the process of generating road networks and building layouts.
  arXiv  Detail & Related papers  (2024-07-10T16:55:01Z)
• RoGs: Large Scale Road Surface Reconstruction with Meshgrid Gaussian [10.50103969885774]
  Road surface reconstruction plays a crucial role in autonomous driving. We propose a novel large-scale road surface reconstruction approach with meshgrid Gaussian, named RoGs. We obtain excellent results in reconstruction of road surfaces in a variety of challenging real-world scenes.
  arXiv  Detail & Related papers  (2024-05-23T09:11:47Z)
• RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving [67.09546127265034]
  Road surface reconstruction helps to enhance the analysis and prediction of vehicle responses for motion planning and control systems. We introduce the Road Surface Reconstruction dataset, a real-world, high-resolution, and high-precision dataset collected with a specialized platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, original point clouds, and ground-truth depth/disparity maps.
  arXiv  Detail & Related papers  (2023-10-03T17:59:32Z)
• LaMAR: Benchmarking Localization and Mapping for Augmented Reality [80.23361950062302]
  We introduce LaMAR, a new benchmark with a comprehensive capture and GT pipeline that co-registers realistic trajectories and sensor streams captured by heterogeneous AR devices. We publish a benchmark dataset of diverse and large-scale scenes recorded with head-mounted and hand-held AR devices.
  arXiv  Detail & Related papers  (2022-10-19T17:58:17Z)
• Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning [60.599223456298915]
  We propose a novel way to integrate deep learning into exploration by leveraging 3D scene completion for informed, safe, and interpretable mapping and planning. We show that our method can speed up coverage of an environment by 73% compared to the baselines with only minimal reduction in map accuracy. Even if scene completions are not included in the final map, we show that they can be used to guide the robot to choose more informative paths, speeding up the measurement of the scene with the robot's sensors by 35%.
  arXiv  Detail & Related papers  (2022-08-17T14:19:33Z)
• Convolutional Recurrent Network for Road Boundary Extraction [99.55522995570063]
  We tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. We design a structured model where a fully convolutional network obtains deep features encoding the location and direction of road boundaries. We showcase the effectiveness of our method on a large North American city where we obtain perfect topology of road boundaries 99.3% of the time.
  arXiv  Detail & Related papers  (2020-12-21T18:59:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.